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Variational Encoder Based Synthetic Alzheimer's data Generation for Deep Learning, XGBoost and Statistical Survival Analysis

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Variational Encoder Based Synthetic Alzheimer's Data Generat...

International Conference on Machine Learning and Applications (ICMLA)

作者： Henry Musto Daniel Stamate Daniel Stahl Department of Computing Goldsmiths Data Science and Soft Computing Lab University of London London United Kingdom Data Science and Soft Computing Lab Department of Computing Goldsmiths Univerisity of London and School of Health Sciences The University of Manchester United Kingdom Department of Biostatistics Institute of Psychiatry Psychology and Neuroscience Kings College London London United Kingdom

ISBN: (数字)9798350374889

ISBN: (纸本)9798350374896

关键词： Deep learning data privacy Analytical models Accuracy Computational modeling Medical services Predictive models data models Alzheimer's disease Synthetic data

Disentangled latent factors for muti-cause treatment effect estimation 6

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Disentangled latent factors for muti-cause treatment effect ...

2024 6th International Conference on Artificial Intelligence Technologies and Applications, ICAITA 2024

作者： Zhao, Zhuoyue Wang, Haotian Xu, Liyang Wang, Qian Tang, Yuhua Department of Intelligent Data Science National University of Defense Technology Changsha410073 China Intelligent Game and Decision Lab. Academy of Military Science Beijing100071 China Institute for Quantum Information State Key Laboratory of High-Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha410073 China

Existing methods estimate treatment effects from observational data and assume that covariates are all confounders. However, observed covariates may not directly represent confounding variables that influence both treatment and outcome. They always include variables that only affect the treatment or the outcome. In addition, for multi-dimensional binary treatments, disentangled methods are mainly designed for binary variables and ignore the impact of multi-cause treatment variables on the inference of latent factors. To address these two issues, based on variable decomposition and proxy inference, we propose the Disentangled Latent Factors for Multi-cause Treatment Estimation (DEMTE) algorithm. It utilizes an identifiable autoencoder to infer and disentangle latent factors based on the joint distribution of variables in observational data. DEMTE evaluates the treatment effect on the disentangled factors. Synthetic experiments and semi-synthetic experiments demonstrate the effectiveness of the inference and disentanglement techniques and our method achieves more accurate treatment effect estimation. © 2024 Institute of Physics Publishing. All rights reserved.

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A Machine Learning Approach for Predicting Deterioration in Alzheimer's Disease

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arXiv 2023年

作者： Musto, Henry Stamate, Daniel Pu, Ida Stahl, Daniel Data Science and Soft Computing Lab Department of Computing Goldsmith University of London London United Kingdom Department of Biostatistics and Health Informatics Institute of Psychiatry Psychology Neuroscience Kings College London London United Kingdom

This paper explores deterioration in Alzheimer’s Disease using Machine Learning. Subjects were split into two datasets based on baseline diagnosis (Cognitively Normal, Mild Cognitive Impairment), with outcome of deterioration at final visit (a binomial essentially yes/no categorisation) using data from the Alzheimer’s Disease Neuroimaging Initiative (demographics, genetics, CSF, imaging, and neuropsychological testing etc). Six machine learning models, including gradient boosting, were built, and evaluated on these datasets using a nested cross-validation procedure, with the best performing models being put through repeated nested cross-validation at 100 iterations. We were able to demonstrate good predictive ability using CART predicting which of those in the cognitively normal group deteriorated and received a worse diagnosis (AUC = 0.88). For the mild cognitive impairment group, we were able to achieve good predictive ability for deterioration with Elastic Net (AUC = 0.76). © 2023, CC BY.

关键词： Deterioration

Balancing Accuracy and Interpretability: An R Package Assessing Complex Relationships Beyond the Cox Model and Applications to Clinical Prediction

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SSRN

SSRN 2024年

作者： Shamsutdinova, Diana Stamate, Daniel Stahl, Daniel Department of Biostatistics and Health Informatics Institute of Psychiatry Psychology and Neuroscience King’s College London London United Kingdom Data Science and Soft Computing Lab Computing Department Goldsmiths University of London United Kingdom School of Health Sciences University of Manchester Manchester United Kingdom

BackgroundAccurate and interpretable models are essential for clinical decision-making, where predictions can directly impact patient care. Machine learning (ML) survival methods can handle complex multidimensional data and achieve high accuracy but require post-hoc explanations. Traditional models such as the Cox Proportional Hazards Model (Cox-PH) are less flexible, but fast, stable, and intrinsically transparent. Moreover, ML does not always outperform Cox-PH in clinical settings, warranting a diligent model validation. We aimed to develop a set of R functions to help explore the limits of Cox-PH compared to the tree-based and deep learning survival models for clinical prediction modelling, employing ensemble learning and nested *** developed a set of R functions, publicly availab.e as the package "survcompare". It supports Cox-PH are Cox-Lasso, and Survival Random Forest (SRF) and DeepHit are the ML alternatives, along with the ensemble methods integrating Cox-PH with SRF or DeepHit designed to isolate the marginal value of ML. The package performs a repeated nested cross-validation and tests for statistical significance of the ML’s superiority using the survival-specific performance metrics, the concordance index, time-dependent AUC-ROC and calibration *** get practical insights, we applied this methodology to clinical and simulated datasets with varying complexities and *** simulated data with non-linearities or interactions, ML models outperformed Cox-PH at sample sizes ≥500. ML superiority was also observed in imaging and high-dimensional clinical data. However, for tabular clinical data, the performance gains of ML were minimal;in some cases, regularised Cox-Lasso recovered much of the ML’s performance advantage with significantly faster computations. Ensemble methods combining Cox-PH and ML predictions were instrumental in quantifying Cox-PH’s limits and improving ML calibration. Traditional models like Cox-PH or Cox

关键词： Prediction models

Learning Sparse Sentence Encoding without Supervision: An Exploration of Sparsity in Variational Autoencoders 6

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Learning Sparse Sentence Encoding without Supervision: An Ex...

6th Workshop on Representation Learning for NLP, RepL4NLP 2021

作者： Prokhorov, Victor Li, Yingzhen Shareghi, Ehsan Collier, Nigel Language Technology Lab. University of Cambridge United Kingdom Department of Data Science & AI Monash University Australia Department of Computing Imperial College London United Kingdom

ISBN: (纸本)9781954085725

It has been long known that sparsity is an effective inductive bias for learning efficient representation of data in vectors with fixed dimensionality, and it has been explored in many areas of representation learning. Of particular interest to this work is the investigation of the sparsity within the VAE framework which has been explored a lot in the image domain, but has been lacking even a basic level of exploration in NLP. Additionally, NLP is also lagging behind in terms of learning sparse representations of large units of text e.g., sentences. We use the VAEs that induce sparse latent representations of large units of text to address the aforementioned shortcomings. First, we move in this direction by measuring the success of unsupervised state-of-the-art (SOTA) and other strong VAE-based sparsification baselines for text and propose a hierarchical sparse VAE model to address the stability issue of SOTA. Then, we look at the implications of sparsity on text classification across 3 datasets, and highlight a link between performance of sparse latent representations on downstream tasks and its ability to encode task-related information. © 2021 Association for Computational Linguistics.

关键词： Natural language processing systems

Predicting risk of dementia with machine learning and survival models using routine primary care records

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Predicting risk of dementia with machine learning and surviv...

2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Langham, John Stamate, Daniel Wu, Charlotte A. Murtagh, Fionn Morgan, Catharine Reeves, David Ashcroft, Darren Kontopantelis, Evan McMillan, Brian University of London Data Science and Soft Computing Lab Department of Computing Goldsmiths London United Kingdom The University of Manchester NIHR School for Primary Care Research Division of Population Health Health Services Research Primary Care School of Health Sciences Manchester United Kingdom

ISBN: (纸本)9781665401265

Worldwide, it is forecasted that 131.5 million people will suffer from dementia by 2050, and the annual cost of care will increase from 818 billion USD in 2016 to 2 trillion USD by 2030, with burgeoning social consequences. Given a timely prediction of a dementia outcome in patients, appropriate mitigating interventions can be applied to reduce risk. However such prediction facilities need to be made availab.e to wider populations, and these facilities cannot rely on specialised, costly and invasive testing (such as neuroimaging, cerebrospinal fluid collection, etc which constitute important instruments used in diagnosis), for interventions to have a meaningful quantitative impact. Hence an emerging need exists for the wider application of prognostic measures which can be deployed using lower cost data sources such as longitudinal records routinely collected by general practices. This paper proposes an efficient prediction modelling approach to the risk of dementia, using CPRD data collected from GP practices in UK, and based on machine learning in particular the Gradient Boosting Machines model combined with a survival model such as the Cox Proportional Hazard, encapsulated in a semi-supervised learning and model calibration methodology. © 2021 IEEE.

关键词： Machine learning

Predicting Alzheimer’s Disease Diagnosis Risk over Time with Survival Machine Learning on the ADNI Cohort

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arXiv 2023年

作者： Musto, Henry Stamate, Daniel Pu, Ida Stahl, Daniel Data Science & Soft Computing Lab Computing Department Goldsmiths College University of London United Kingdom Division of Population Health Health Services Research & Primary Care School of Health Sciences University of Manchester United Kingdom Institute of Psychiatry Psychology and Neuroscience Biostatistics and Health Info-matics Department King’s College London London United Kingdom

The rise of Alzheimer’s Disease worldwide has prompted a search for efficient tools which can be used to predict deterioration in cognitive decline leading to dementia. In this paper, we explore the potential of survival machine learning as such a tool for building models capable of predicting not only deterioration but also the likely time to deterioration. We demonstrate good predictive ability (0.86 C-Index), lending support to its use in clinical investigation and prediction of Alzheimer’s Disease risk. © 2023, CC BY.

关键词： Machine learning

Predicting Risk of Dementia with Survival Machine Learning and Statistical Methods: Results on the English Longitudinal Study of Ageing Cohort

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arXiv 2023年

作者： Stamate, Daniel Musto, Henry Ajnakina, Olesya Stahl, Daniel Data Science & Soft Computing Lab Computing Department Goldsmiths College University of London United Kingdom Division of Population Health Health Services Research & Primary Care School of Health Sciences University of Manchester United Kingdom Institute of Psychiatry Psychology and Neuroscience Biostatistics and Health Informatics Department King’s College London United Kingdom Department of Behavioural Science and Health Institute of Epidemiology and Health Care University College London United Kingdom

Machine learning models that aim to predict dementia onset usually follow the classification methodology ignoring the time until an event happens. This study presents an alternative, using survival analysis within the context of machine learning techniques. Two survival method extensions based on machine learning algorithms of Random Forest and Elastic Net are applied to train, optimise, and validate predictive models based on the English Longitudinal Study of Ageing – ELSA cohort. The two survival machine learning models are compared with the conventional statistical Cox proportional hazard model, proving their superior predictive capability and stability on the ELSA data, as demonstrated by computationally intensive procedures such as nested cross-validation and Monte Carlo validation. This study is the first to apply survival machine learning to the ELSA data, and demonstrates in this case the superiority of AI based predictive modelling approaches over the widely employed Cox statistical approach in survival analysis. Implications, methodological considerations, and future research directions are discussed. © 2023, CC BY.

关键词： Machine learning

EdgeDis: Enabling Fast, Economical, and Reliable data Dissemination for Mobile Edge computing

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arXiv 2023年

作者： Li, Bo He, Qiang Chen, Feifei Lyu, Lingjuan Bouguettaya, Athman Yang, Yun The College of Arts Business Law Education and Information Technology Victoria University MelbourneVIC3122 Australia The National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab. Cluster and Grid Computing Lab. School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China The Department of Computing Technologies Swinburne University of Technology MelbourneVIC3122 Australia The School of Information Technology Deakin University Geelong Australia SONY AI Inc. Tokyo108-0075 Japan The School of Computer Science University of Sydney CamperdownNSW2006 Australia

Mobile edge computing (MEC) enables web data caching in close geographic proximity to end users. Popular data can be cached on edge servers located less than hundreds of meters away from end users. This ensures bounded latency guarantees for various latency-sensitive web applications. However, transmitting a large volume of data out of the cloud onto many geographically-distributed web servers individually can be expensive. In addition, web content dissemination may be interrupted by various intentional and accidental events in the volatile MEC environment, which undermines dissemination efficiency and subsequently incurs extra transmission costs. To tackle the above challenges, we present a novel scheme named EdgeDis that coordinates data dissemination by distributed consensus among those servers. We analyze EdgeDis’s validity theoretically and evaluate its performance experimentally. Results demonstrate that compared with baseline and state-of-the-art schemes, EdgeDis: 1) is 5.97x - 7.52x faster;2) reduces dissemination costs by 48.21% to 91.87%;and 3) reduces performance loss caused by dissemination failures by up to 97.30% in time and 96.35% in costs. © 2023, CC BY-NC-SA.

关键词： Efficiency